Bandingkan metode

	Autoregresi Vektor (VAR)	Uji Kausalitas Granger	Structural Vector Autoregression (SVAR)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1980	1969	1980
Pencetus	Christopher A. Sims	Clive W. J. Granger	Sims (1980); identification schemes by Blanchard & Quah (1989)
Tipe	Multivariate time-series model	Causality test (F-test on VAR)	Multivariate time series model
Sumber perintis	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Alias	VAR, VAR model, vector autoregressive model, multivariate autoregression	Granger test, GC test, predictive causality test, Granger non-causality test	SVAR, structural vector autoregression, identified VAR, structural VAR model
Terkait	5	5	5
Ringkasan	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.